Good and Bad Days

Fluctuations in the Burden of Informal Dementia Caregivers, an Experience Sampling Study

Abstract

Background 

Informal dementia caregivers (IDCs) are often confronted with important fluctuations in care-related burden, commonly described as “good and bad days.” These fluctuations are overlooked by traditional questionnaires focusing on the average experience. The experience sampling method (ESM) is based on the repeated collection of data in everyday life, thereby allowing the description of day-to-day fluctuations in IDC burden, and the identification of their correlates. ESM studies are still scarce among IDCs, with none focusing on day-to-day fluctuations in burden.

Objectives 

This ESM study aimed to examine day-to-day fluctuations in the burden of IDCs and test their associations with six moment-to-moment predictors.

Methods 

Primary IDCs (N = 26, median age = 68 years, 77% women, 73% spouses) volunteered to answer questions about their daily burdens, patients’ memories and behavioral problems (MBP), caregivers’ MBP-related distress, psychological distress, self-efficacy and positive affects, and relationship quality; volunteers did this every evening for 2 weeks on a touchpad, resulting in 206 measures. Data were analyzed with multilevel linear regression.

Results 

Day-to-day fluctuations covered about two thirds of the total variance for most study variables. All six predictors had a significant bivariate relation with daily burden, explaining 15%–32% of its fluctuations, with significant differences between caregivers in the strength of these relations. The best multivariate model explained 51% of the day-to-day fluctuations in burden. It included caregiver MBP-related distress, psychological distress, and relationship quality.

Discussion 

This innovative study of IDC burden shows that day-to-day fluctuations are an important part of caregivers’ real-life experiences and that half of this variability is predicted by currently understudied factors. Inviting caregivers to monitor clinical outcomes daily over 1 or 2 weeks could help tailor interventions to their individual needs and also empower them.

Because of population aging, the prevalence of dementia has been increasing substantially over the past decades. In 2010, dementia already affected more than 35 million people worldwide, and these numbers are expected to double every 20 years (^{Prince et al., 2013}). In Switzerland, more than 110,000 persons currently live with dementia; this number is expected to rise to 200,000 in 2030 (^{Höpflinger, Bayer-Oglesby, & Zumbrunn, 2011; Office Fédéral de la Santé Publique [OFSP], 2016}). About half of the persons with dementia currently live at home (the other half in nursing homes), 43% require occasional support, 47% daily support, and 10% continuous support; most of this support is provided by informal caregivers (^{Association Alzheimer Suisse, 2010; Kraft, Marti, Werner, & Sommer, 2010}), such as the spouse or a child of the person with dementia (^{Perrig-Chiello, Höpflinger, & Schnegg, 2010}). These figures are similar to those of other European countries (e.g., ^{EUROFAMCARE, 2006}).

In Switzerland, the follow-up of community-dwelling persons with dementia is conducted first by their general practitioners and then by the interdisciplinary and specialized teams of local memory clinics. However in 2004, only one third of the persons with dementia had received a formal diagnosis, and more recent figures are currently lacking (^{OFSP, 2016}). The care trajectory is predominantly based on individual decision-making in the absence of clear guidelines and systematic follow-up. The availability of homecare nurses, daycare centers, short stays in nursing homes, night respite, or home-based support by volunteers varies across regions but, in many cases, is not specialized for persons with dementia (^{OFSP, 2016}). Informal dementia caregivers (IDCs) already are one of the largest groups of caregivers served by homecare nurses in Switzerland (more than one third) (^{Perrig-Chiello et al., 2010; Perrig-Chiello, Höpflinger, & Hutchison, 2011}). However, half of the caregivers report needing more support than they currently have—particularly home-based caregivers (^{OFSP, 2016})—and care coordination is one of their core duties (^{Perrig-Chiello et al., 2010}), as is also the case in other European countries (^{EUROFAMCARE, 2006}).

Among all informal caregivers, IDCs commonly experience a particularly high burden—and are at a significantly higher risk for negative consequences—on health and quality of life (^{Brodaty & Donkin, 2009; Perrig-Chiello et al., 2010; Schulz & Martire, 2004}). IDC burden is further known to increase the risks of both early institutionalization and mistreatment of the care recipient (^{Koerner, Shirai, & Kenyon, 2010}). Substantial evidence has confirmed the large day-to-day fluctuations in dementia symptoms (particularly in Lewy body, vascular, and Parkinson’s disease dementia and also in other dementia; these fluctuations seem to increase the burden of IDCs; see the review by ^{Lee, Taylor, & Thomas, 2012}). However, researchers have only recently started to investigate day-to-day fluctuations in IDC experience and their correlates. Pioneering work in this direction includes two intervention projects led by S. H. Zarit at Penn State University (e.g., ^{Zarit et al., 2011; Zarit, Kim, Femia, Almeida, & Klein, 2014}) and R. van Knippenberg at the University of Maastricht in the Netherlands (^{van Knippenberg, de Vugt, Ponds, Myin-Germeys, & Verhey, 2016; van Knippenberg et al., 2017}). Our study aimed to expand this innovative line of research focusing on burden.

Burden and its determinants have been conceptualized within the stress process model illustrated in Figure 1 (^{Pearlin, Mullan, Semple, & Skaff, 1990}). This model proposes that IDC core outcomes, such as burden, are influenced by four types of interacting stressors: (a) background and contextual factors, (b) primary dementia-related stressors, (c) secondary stressors related to role strains, and (d) secondary stressors related to intrapsychic strains. Despite this recognition that multiple interacting factors impact IDC experience, research has predominantly examined these factors separately. Yet a recent literature review of multivariate models predicting burden (based on 32 studies; ^{van der Lee, Bakker, Duivenvoorden, & Dröes, 2014}) highlighted the importance of patient memories and behavioral problems (MBP), along with caregiver neuroticism (i.e., propensity to negative affect) and self-efficacy.

FIGURE 1: The stress process model (adapted from ^{Pearlin et al., 1990}; used with permission). Boxes in gray indicate variables used in this study and variables in italics include day-to-day fluctuations.

As underlined by ^{Koerner et al. (2010, p. 561)}, “most research on caregivers well-being takes a snapshot approach to data collection; that is, data are collected on only one occasion or are collected longitudinally, but with long intervals between waves (e.g., 1 year). As such, most studies […] miss the day-to-day fluctuations.” There is evidence that the day-to-day fluctuations are responsible for one fourth to one half of the variance in outcomes (27% for subjective burden, 34% for physical symptoms, and 43% in depressive symptoms) and more than half of the variance in stressors (55% for care-recipient behavioral problems or 61% for family disagreements regarding care; ^{Koerner & Kenyon, 2007}). These results stem from daily reports collected for 8 days from 62 caregivers of individuals with diverse health problems (including cognitive impairment in an unspecified number of cases), which confirm that the experience of good and bad days is core to caregiving in general. The day-to-day fluctuations in caregivers’ outcomes and stressors may be even more pronounced in IDCs, given the important fluctuations in the symptomatology of dementia patients. A recent study of 173 IDCs using daycare services showed that day-to-day fluctuations in negative affect were substantial and could even outweigh differences between IDCs (this was the case for anger and to a lesser extent also for depression; ^{Liu, Kim, Almeida, & Zarit, 2015}). Investigating the stress process model with a finer time resolution, in other words, at the state level, may therefore shed light on predictors of burden with a more proximal influence—which may or not be similar to those identified to date at a trait level. We therefore aimed to consider both well-established predictors with potential day-to-day fluctuations, such as patient MBP, as well as caregiver self-efficacy or positive affects (as reduced positive affect is associated with the stable personality trait of neuroticism). We further aimed to include potentially promising predictors, such as caregiver distress related to patient MBP or general psychological distress, as well as relationship quality, in order to cover the diverse type of stressors involved in Pearlin’s model (see Figure 1).

Ignoring day-to-day fluctuations in IDC outcomes precludes the identification of factors affecting these fluctuations, which may be important intervention targets as they are highly proximal to the daily burden experience. The experience sampling method (ESM) provides repeated measures or a regular monitoring of the subjective experience occurring in natural settings and is, thereby, particularly well suited to assess human processes. ESM involves the collection of repeated data via paper format or an electronic device, in real life, as phenomena occur or soon after (for a review, see ^{Shiffman, Stone, & Hufford, 2008}). ESM is still largely underused in nursing (^{Reuschenbach, 2006}) and related fields, such as public health (^{Jones & Johnston, 2011}) or mental health (^{Myin-Germeys et al., 2009}). Advantages of ESM include studying individual differences with more reliable and ecologically valid data, describing the intraindividual variability and change patterns, as well as investigating correlates, predictors, and consequences of daily experience (^{Mehl, 2012}).

ESM is often used to limit retrospective bias when reporting subjective experience or behavior. It was shown that IDCs reported greater stress in ESM than in a traditional 1-point questionnaire whereas no difference was found between the two types of measures in noncaregiving elderly participants (^{Fonareva, Amen, Ellingson, & Oken, 2012}). Most interestingly, ESM allows the investigation of day-to-day associations between specific events and outcomes. Two recent projects led by S. H. Zarit used daily measures to assess the effects of daycare services. The first project (^{Zarit et al., 2011}) compared daily measures obtained in 121 IDCs before their care recipient attended daycare services for the first time and after 1 and 2 months of attendance on days with and without daycare services. Care-related stress decreased significantly over time on days with daycare compared to those without—particularly for IDCs confronted to high levels of behavioral problems. This decrease was related to reduced care-related stress during the day, but also less behavioral and sleep problems of the person with dementia in the evening and night following daycare use. The second project (^{Liu et al., 2015; Zarit et al., 2014}) similarly compared daily assessments collected over eight consecutive days in 173 IDCs using daycare services at least twice a week. IDCs reported less care-related stressors and health symptoms on days with daycare, along with more noncare stressors and positive events—both mostly work related. Besides, IDC daily affects and health symptoms were strongly associated with daily exposure to care and noncare stressors as well as positive events. Daycare further buffered the effect of care and noncare stressors on depression.

Finally, ESM allows testing whether day-to-day associations are moderated by characteristics of the caregiver or his or her environments. Another daily diary study conducted in 25 dementia caregivers (^{Mausbach, Harmell, Moore, & Chattillion, 2011}) showed that the level of burden at study entry moderated the relationship between leisure satisfaction and both positive and negative effects. For caregivers with a higher burden, doing a more satisfactory leisure activity was associated with a stronger increase in positive affect and reduction in negative effect, whereas these associations were weaker among caregivers with lower burden. Thus, caregivers with higher burden seem to benefit more from their involvement in enjoyable activities, suggesting behavioral activation could be a particularly efficient intervention for this specific subgroup. Our study aims to expand on these highly stimulating results, focusing on subjective burden—a key outcome for IDC.

Aims

With an ESM design, we aimed to examine (a) the amount of day-to-day fluctuations in burden and (b) the day-to-day predictors of daily burden within the stress process model. In particular, we aimed to examine with a microlongitudinal approach the respective contribution of primary stressors (patient MBP, relationship quality), secondary stressors related to intrapsychic strains (caregiver distress related to patient MBP, low self-efficacy, and psychological distress), and moderators (caregiver positive affect).

METHODS

This study is part of a larger quasiexperimental intervention study. The data presented here were collected from October 2014 to May 2015 as baseline assessment before the participation in a psychoeducative intervention.

Sample

We recruited a convenience sample of 26 IDCs through service providers in the field of dementia (Alzheimer Association, home care nurses, memory clinics, daycare centers). Participants volunteered for a free psychoeducative intervention focusing on stress management and consisting of 15 weekly sessions in group format, along with pre- and postintervention interviews and daily short reports on a tablet. Inclusion criteria were (a) being the primary caregiver of a person living with a diagnosis of dementia and (b) caring for this person since at least 6 months. Exclusion criteria were (a) insufficient French language skills, (b) low caregiver burden (score of <10 on the Zarit Burden Interview), and (c) no patient MBP.

Procedure

The study was approved by the local ethics review board (Commission cantonale vaudoise d'éthique de la recherche sur l'être humain). Written informed consent was obtained from each IDC after oral and written information provided by one of the authors. During an individual interview with a trained researcher, participants received ESM training and completed questionnaires. The ESM training involved an introduction to the handling of the touchpad (Samsung Galaxy Tab4) on which all ESM questions were answered and the completion of the first ESM data point with an explanation of each question. Each participant then received a touchpad to take home and was instructed to answer the ESM questions every evening for 2 weeks. Each participant was offered the possibility to have an acoustic signal, reminding them to reply at the time of their choice, but all of them declined. By way of an application specifically developed for this study with a focus on usability for older adults, questions were presented one at a time on the screen of the touchpad, written in large characters, along with a slider to give the answer (see Figure 1). To avoid missing data, participants could only move to the next question after having answered the current one. The responses were automatically stored on a server for participants with a Wi-Fi connection. For participants without Wi-Fi, responses were stored in the touchpad and later sent to the server by the researcher when the touchpad was returned. After the 2 weeks of ESM, each participant provided brief feedback on his or her experience using standardized questions.

Variables and Measurement

We collected three types of data. First, we used standardized questionnaire measures to assess eligibility (caregiver burden and patient MBP). Second, we collected ESM measures of caregiver burden, patient MBP, caregiver MBP-related distress, relationship quality, caregiver psychological distress, caregiver self-efficacy, and caregiver positive effect to meet the study objectives. Third, we collected data about the acceptability of ESM for the participants.

Screening Measures

Caregiver burden We measured the subjective burden (i.e., caregiver perceived emotional, social, and financial loads associated with care provision) with the Zarit Burden Interview (^{Zarit, Orr, & Zarit, 1985}), a well-validated and widely used 22-item questionnaire, with scores of >18 indicating an important burden and scores of >32 indicating a severe one (^{Hébert, Bravo, & Préville, 2000}).

MBP We measured the MBP (i.e., the cognitive symptoms of dementia, such as forgetfulness, and the behavioral or psychiatric disturbances related to dementia, such as aggression, sleep disturbances, or depression) with the Revised MBP Checklist (^{Teri et al., 1992}), a questionnaire which measures the frequency of 24 MBP in the preceding week, between 0 = never and 4 = daily.

ESM Measures

As is typical in ESM studies, we used a limited number of questions to measure each construct, to prevent overloading the participants and maintain compliance over time. The chosen questions were either (a) the most general question of a validated questionnaire when unidimensional (e.g., caregiver burden), (b) one question for each dimension of the validated questionnaire when multidimensional (e.g., MBP), or (c) a single question commonly used for the global estimation of the construct of interest (e.g., self-efficacy). Participants were asked to answer nine ESM questions on their current day (“Today…”), on a visual analogue scale yielding a score between 0 = not at all and 100 = extremely. Figure 2 provides examples of data records.

FIGURE 2: Examples of data records for four informal caregivers of persons with dementia.

Caregiver burden This was measured with one question adapted from the Zarit Burden Interview: Did you feel that caring for [name of the person with dementia] was a burden?

Patient MBP This was measured with three questions adapted from the MBP Checklist (^{Teri et al., 1992}): (a) Did [name] show memory problems (such as repeating things, losing things, not completing a task, not remembering events or persons, concentration difficulties)? (b) Did [name] show behavior problems (such as breaking things on purpose, verbal aggression, threats to harm others, night wandering, opposition or provocation)? (c) Did [name] show depressive symptoms (such as sadness or hopelessness, threats of self-harm, speaking of dying, suffering from loneliness, uselessness or worthlessness)?

Caregiver MBP-related distress This construct refers to the extent to which the caregiver feels disturbed or upset by the MBP of the person with dementia as proposed by ^{Teri and colleagues (1992)}. It was measured with one question adapted from the MBP Checklist (^{Teri et al., 1992}): How much did [name]’s MBP disturb or upset you?

Relationship quality This construct refers to the subjective appreciation by the caregiver of the quality of his or her relationship with the care recipient (^{Adams, McClendon, & Smyth, 2008}). As in the cited study, it was measured with one question: Did you have a good relationship with [name]?

Caregiver psychological distress This construct refers to the extent to which the caregiver experiences common symptoms of mental disorders: depression, anxiety, anger, and cognitive disturbance (^{Ilfeld, 1976}). It was measured with one question adapted from the Ilfeld Psychiatric Symptoms Index: Did you feel depressed, anxious, irritated, or confused?

Caregiver self-efficacy This construct developed by ^{Bandura (1977)} refers to the degree of confidence caregivers have in their ability to assume their roles. As suggested by this author, it was measured with one question: Did you feel confident in your caregiver role?

Caregiver positive affect This construct refers to the amount of positive emotions experienced by the caregiver, such as happiness, cheerfulness, and enjoyment (^{von Känel et al., 2014}). It was measured with one question: Did you enjoy your day?

Acceptability of ESM

We used eight questions to measure the acceptance of ESM (e.g., “I found it difficult to understand the questions”; “I found it difficult to give my answers on the touchpad”; “I found it difficult to handle the touchpad”; “Overall, answering the questions on the tablet was a pleasant experience”; “Overall, answering the questions on the tablet was a stressful experience”; “I would be interested in participating in similar studies in the future”; “I would recommend others to participate in a similar study”). All questions were rated between 1 = not at all or very little and 5 = extremely.

Statistical Analyses

Screening variables and questions on the acceptability of ESM were analyzed with descriptive statistics. For ESM variables, there were no missing data, as participants had to answer each question on the touchpad in order to move to the next question. (We did not consider days where no data were provided as missing because the models we estimated were not time dependent.) As the measurement occasions (within-person, Level 1) were nested within the participants (Level 2), data were analyzed with multilevel regression with full-maximum likelihood estimation (using Hierarchical Linear Modeling, HLM 6; ^{Bryk, Raudenbush, & Congdon, 1994}). Day-to-day fluctuation was assessed for each variable of interest by entering the variable as outcome in an empty model in order to estimate the amount of variance found on the within-person (Level 1) and between-persons (Level 2) levels. The association of each daily predictor with caregiver burden was first tested within separate univariate models with burden as outcome and the predictor of interest on Level 1 (group mean-centered, random slope). Second, to identify the most important predictors while considering all predictors, we conducted a multivariate analysis using the same model with a stepwise forward approach; we entered each predictor in the model consecutively in order of decreasing effect size until the model improvement was no longer significant.

Results

Participant Characteristics

The characteristics of the 26 IDCs who participated in the study and of their care recipients are presented in Table 1. Most participants were women, spouses of the patients, with a median age of 68 years. Most patients were men and had a diagnosis of dementia, with a median age of 78 years. IDCs predominantly lived in the same household as patients, had been providing care for a median duration of 3 years, and were currently in charge of the patient for a median of 6 days a week.

TABLE 1: Participant Characteristics

Feasibility of ESM

Ninety-three percent of the recruited caregivers (n = 26 out of 28) completed the 2-week ESM protocol. ESM data overall included 206 measures (per participant: M = 12.4 measures, SD = 3.73, with 77% providing at least 10 measures). Only caregivers who did not have daily contacts with the patient provided fewer than 10 measures. The feasibility was therefore excellent.

Regarding acceptability, most participants found it easy to understand the questions (none or few difficulties: 96%), give their answers on the touchpad (none or few difficulties: 96%), and handle the touchpad (none or few difficulties: 87%). Most participants found the experience pleasant (moderately or very: 96%) and not stressful (not at all or a little: 96%). Most of them said they would recommend others to participate in a similar study (moderately to extremely: 91%) and would be interested in participating in a similar study in the future (moderately to extremely: 86%). The median time required to answer all questions was 10.9 minutes (quartiles: first = 7.4, third = 11.5); the substantial differences between participants seemed to be related to the length of the answers provided for open-ended questions.

Descriptives

Responses to the Zarit Burden Questionnaire showed that the caregiver burden was, on average, severe (M = 40.5, SD = 14.22), with 69% of participants reporting severe burden and 19% with moderate burden and with a moderate frequency of patient MBP (M = 1.6, SD = 0.52) and caregiver MBP-related distress (M = 1.7, SD = 0.70). As to ESM variables, on average, across the 2 weeks (Table 2, average level), caregivers reported moderate levels of daily burden (95% CI [30.3, 44.0]), MBP frequency (95% CI [31.8, 40.5]), MBP-related distress (95% CI [31.3, 44.2], and psychological distress (95% CI [21.7, 24.6]), along with high levels of self-efficacy (95% CI [60.9, 73.6]) and positive affect (95% CI [63.6, 74.6]), as well as good relationship quality (95% CI [65.4, 76.9]).

TABLE 2: Average Level and Within-Person and Between-Persons Variances

Day-to-Day Variability

Day-to-day fluctuations largely exceeded interindividual differences for nearly all ESM variables, with around two thirds of the variance observed on the within-person level (Table 2, % variance explained; see Document, Supplemental Digital Content 1, https://links.lww.com/NRES/A266). The only exception was self-efficacy, with half of the within-person variance explained. This finding indicates that, for all study variables, traditional questionnaires administered at one point in time overlook half to two thirds of the variance in daily experience of IDC.

Prediction of Caregiver Daily Burden

Each of the six predictors separately showed a significant and substantial association with daily burden in the expected direction (Table 3; see Document, Supplemental Digital Content 2, https://links.lww.com/NRES/A267). Caregivers experienced a higher burden on days where, compared to their own average, they faced more MBP in their care recipient and were more distressed by these MBP, had a worse relationship quality, and experienced more psychological distress, less positive effect, and lower self-efficacy. The largest effect sizes were observed for caregiver MBP-related distress and relationship quality, which both explained about 30% of the day-to-day variance in burden. Caregiver psychological distress, positive affect, and patient MBP also explained about one fourth of the variance in burden; self-efficacy had the smallest effect size. For most predictors, caregivers showed different individual patterns of association with burden, as indicated by the fact that all slopes, except for positive affect and self-efficacy, entailed significant variance between individuals. This finding indicates that, although positive affect and self-efficacy affected burden in a similar way for all caregivers, each of the other predictors could have a strong influence on burden for some caregivers and a weak one for others.

TABLE 3: Prediction of Daily Burden

We then entered each of these predictors consecutively in a multivariate model in order of decreasing effect size. Table 3 summarizes the results of the multivariate models. (Detailed results are in the Document, Supplemental Digital Content 3, https://links.lww.com/NRES/A268.) We started with MBP-related distress, then added relationship quality, resulting in a significant improvement of the model. We further added psychological distress—which also significantly improved the model—and positive effect, again significantly improving the model. Patient MBP and self-efficacy did not further improve the prediction of daily burden so that they were excluded. The final model with four predictors explained 55% of the within-person variance and 33% of the total variance.

DISCUSSION

This innovative study of dementia caregivers’ experience showed that day-to-day fluctuations in each caregiver largely exceeded differences between caregivers for nearly all study variables and particularly for subjective burden. Each of the six predictors showed a significant bivariate relation with daily burden, explaining as much as 13%–33% of daily fluctuations. The best multivariate prediction model included caregiver MBP-related distress, relationship quality, caregiver psychological distress, and positive affect as predictors of caregiver daily burden. This model explained half of the day-to-day fluctuations in burden.

The large day-to-day variability in dementia cognitive symptoms is well established (^{Lee et al., 2012}). Fluctuations between “good days” and “bad days” in IDC experience are also well known from health professionals and have been documented in qualitative studies (e.g., ^{Rockwood, Fay, Hamilton, Ross, & Moorhouse, 2014}) as resulting from the fluctuations in care recipient cognition, functionality, and behavior. Our quantitative study established that day-to-day fluctuations in dementia caregiving experience represent as much as two thirds of the variance for burden as well as for other variables of the stress process model. Our results confirm and expand those of the single study, which explicitly assessed day-to-day fluctuations in 173 IDCs using daycare services; these fluctuations represented 51% of the variance for anger and 27% for depression (^{Liu et al., 2015}). Our results also suggest that caregivers of a person with dementia experience more day-to-day fluctuations (63% of variance) compared to those of persons with mixed conditions (27%; ^{Koerner & Kenyon, 2007}), although systematic comparisons with identical methods are needed to test this hypothesis. Taken together, these findings demonstrate that traditional questionnaire measures neglect an important part of the caregiving experience and a substantial amount of variance, particularly for subjective burden. Clinicians and researchers are thus encouraged to use structured diaries to monitor, over time, outcomes of interest, such as burden, instead of traditional questionnaires or global verbal reports. Conducting such a monitoring, for example, over 2 weeks, could help identify the most important stressors for a specific caregiver, as well as the most problematic times of the day or week. This information would support the individualized planning of respite, assistance, and other specific interventions (e.g., communication training).

The five ESM studies conducted in IDCs to date, including ours, all showed that it was feasible to repeatedly collect structured information about outcomes of interest, using either a paper-and-pencil diary (e.g., ^{Mausbach et al., 2011}), electronic devices—as in our study (e.g., ^{van Knippenberg et al., 2017})—or phone interviews (e.g., ^{Zarit et al., 2011}). Our study and van Knippenberg et al.’s study further confirmed that data collection with electronic devices was well accepted, although most IDCs were older adults with limited familiarity with technological devices. Despite the fact that most participants reported a moderate to severe burden at study entry, the data collection was not experienced as stressful. The ESM studies varied in data collection’s frequency (1–10 times a day) and length (1–14 days). As also observed in our study, overall, participation rates were high (e.g., 81%, ^{Koerner et al., 2010}; 97%, ^{Liu et al., 2015}), dropout rates were low (e.g., 2%, ^{Koerner et al., 2010}), and compliance rates were very high (e.g., 98% for eight daily phone interviews, ^{Liu et al., 2015}; 90% for four paper-and-pencil measures per day during 14 days, ^{Mausbach et al., 2011}; 79% for 10 measures per day during 6 days on an electronic device, ^{van Knippenberg et al., 2017}). The available evidence thus confirms that IDCs are willing and able to regularly monitor some clinical outcomes of interest for a week or two and that doing so is not experienced as stressful.

From a theoretical viewpoint, the substantial bivariate relations observed between each of our predictors and burden confirms the importance of different primary stressors (patient MBP, relationship quality), as well as different secondary stressors (caregiver distress related to patient problems, low self-efficacy, or psychological distress) included in ^{Pearlin et al.’s (1990)} model. In addition, our findings confirmed the importance of an additional and understudied moderator: positive affect. Notably, two of the four predictors found significant in a multivariate model were secondary stressors, which supports ^{Pearlin et al.’s (1990)} assertion that “[…] secondary stressors are every bit as powerful as those that are primary in producing stress outcomes” (p. 588). Large samples and slightly longer series would allow testing more fully the stress process model by including causal paths and mediator effects.

Furthermore, our findings showed that predictors related to burden in previous research focusing on interindividual differences (see the review by ^{van der Lee et al., 2014}) are also associated with the day-to-day fluctuations in burden; this is the case for patients’ MBP (which was found to be significantly associated with burden in 79% of the 28 questionnaire studies reviewed), caregivers’ self-efficacy (86% of seven studies), and caregivers’ psychological distress (61% of 19 studies). However, three of the four best predictors identified in our study have rarely been integrated in the complex models of burden prediction and have, to date, been examined only by few questionnaire studies. This is the case for relationship quality (e.g., the questionnaire study by ^{Campbell et al., 2008}), caregivers’ distress related to patient MBP, as well as caregivers’ positive affect (e.g., the questionnaire studies by ^{Papastavrou et al., 2011; Reis, Gold, Andres, Markiewicz, & Gauthier, 1994}). Although some predictors may explain both interindividual differences and day-to-day fluctuations in burden, focusing on daily fluctuations seems to reveal more proximal factors involved in the experience of burden and may thereby identify new and most promising intervention targets.

Our ESM data have further shown that different IDCs may have different individual patterns of association between most of the considered factors and burden. This finding indicates that patient MBP may be most strongly related to daily burden for some caregivers, whereas for others, the strongest association may be with relationship quality or positive affect. Identifying profiles of burden predictors or, in other terms, groups of caregivers with similar risk factors for increased burden could help provide better targeted interventions.

Our findings have important implications for both research and clinical practice. From a research perspective, having established that day-to-day fluctuations are an important reality of the caregiving experience paves the way for testing whether these fluctuations could also be a risk factor for exhaustion or other longer-term adverse consequences of caregiving. Such a hypothesis is in line with evidence that affective reactivity to stressors is a risk factor for negative health outcomes (^{Charles, Piazza, Luong, & Almeida, 2009}). Furthermore, lowering burden has been a common intervention target for decades—which has, however, often been difficult to achieve (^{Acton & Kang, 2001; Brodaty, Draper, & Low, 2003})—possibly due to the ongoing challenges associated with the care of people with dementia. In this context, reducing the amount of day-to-day fluctuations may be more achievable and relevant as it may increase caregivers’ sense of control over their situations and consequently reduce their subjective stress, as well as the associated risk of negative health consequences.

From a clinical perspective, daily reports may first raise both caregivers and clinicians awareness of the amount of fluctuations experienced from day to day, thereby validating both the challenges faced by each caregiver and the diversity of experiences each caregiver goes through. Examination of daily reports over a period of 2 weeks may further foster caregiver and clinician insight in the contexts, events, and timings associated with improvements or worsening of clinical outcomes such as lowered or increased burden. This knowledge is essential to support the planning of interventions tailored to the individual needs and challenges of each caregiver. The procedure would further foster caregiver empowerment by enabling them to better identify their own needs and offering them a practical way to be an active partner in intervention planning.

Limitations

This study had some limitations. As already mentioned, our sample was small (N = 26). Simulation studies of multilevel regression analyses have shown that such a limited number of participants (^{Browne & Draper, 2000} [N = 24–30]; ^{Maas & Hox, 2005} [N = 30]) does not affect the accuracy of the parameters but can lead to slightly underestimated standard errors and, thus, slightly increased effective α (9% instead of 5% for 24–30 participants as in our case; ^{Browne & Draper, 2000}). Nevertheless, ^{Maas and Hox (2005)} demonstrated that this bias in negligible for the standard errors of fixed parameters, which were of primary interest in this study. These authors have further confirmed that the number of measurements per participant has no effect on bias even for as few as five measures per participant. Therefore, further studies using a similar design should aim at increasing the number of participants rather than the number of measurements per participants. As in previous research focusing on interindividual differences, our predictors were largely interdependent. Thus, only four of them were significant in a multivariate model, although all six had substantial bivariate associations with daily burden. A more promising approach to capture the complex interrelations at play is to test models including mediators and causal relations with structural equation modeling. Such models could also be used to test whether caregiver (e.g., neuroticism) or background (e.g., family composition) characteristics moderate the day-to-day associations of diverse predictors with burden. A larger sample size would be needed to conduct such analyses. In conclusion, larger ESM studies of caregiver burden are needed to shed more light on these processes. Second, so as to not overload the IDC, most constructs were measured with a single question, which precludes reliability assessment, and other potentially relevant variables were not assessed; in particular, role strains such as disagreements within the family regarding provision of care (^{Koerner & Kenyon, 2007}). Future studies need to investigate the most efficient data collection strategy to achieve sufficient numbers of measures without overloading the participants. Third, although the observed effects were statistically significant and explained substantial amounts of variance, establishing their clinical relevance would require studies linking them to long-term outcomes such as caregiver health or quality of life.

Conclusion

This study adds to an emerging research line showing that ESM can shed light on an underresearched yet rich part of the dementia caregiving experience by capturing the substantial day-to-day fluctuations and allowing the examination of stress processes as they occur in the real life of caregivers. It thereby paves the way for testing more complex models in order to stimulate the development of interventions for the prevention of caregiver exhaustion and particularly the tailoring of interventions to caregivers’ individual risk factors. It also indicates that the daily monitoring of relevant outcomes for a couple of weeks is a feasible and well-accepted method for clinicians aiming to gain insight in the most relevant stressors for each caregiver.